Spatial and temporal rainfall variability in mountainous areas:: A case study from the south Ecuadorian Andes

被引:312
作者
Buytaert, Wouter
Celleri, Rolando
Willems, Patrick
De Bievre, Bert
Wyseure, Guido
机构
[1] Katholieke Univ Leuven, Lab Soil & Water Management, B-3001 Louvain, Belgium
[2] Univ Cuenca, Programa Manejo Agua & Suelo, Cuenca, Ecuador
[3] Katholieke Univ Leuven, Hydraul Lab, B-3001 Louvain, Belgium
关键词
rainfall variability; mountain environments; interpolation; kriging; thiessen; Ecuador;
D O I
10.1016/j.jhydrol.2006.02.031
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Particularly in mountain environments, rainfall can be extremely variable in space and time. For many hydrological applications such as modelling, extrapolation of point rainfall measurements is necessary. Decisions about the techniques used for extrapolation, as well as the adequacy of the conclusions drawn from the final results, depend heavily on the magnitude and the nature of the uncertainty involved. In this paper, we examine rainfall data from 14 rain gauges in the western mountain range of the Ecuadorian Andes. The rain gauges are located in the western part of the rio Paute basin. This area, between 3500 and 4100 m asl, consists of mountainous grasslands, locally called paramo, and acts as major water source for the inter-Andean valley. Spatial and temporal rainfall patterns were studied. A clear intraday pattern can be distinguished. Seasonal variation, on the other hand, is low, with a difference of about 100 mm between the dryest and the wettest month on an average of about 100 mm month(-1), and only 20% dry days throughout the year. Rain gauges at a mutual distance of less than 4000 m are strongly correlated, with a Pearson correlation coefficient higher than 0.8. However, even within this perimeter, spatial variability in average rainfall is very high. Significant correlations were found between average daily rainfall and geographical location, as well as the topographical parameters slope, aspect, topography. Spatial interpolation with thiessen gives good results. Kriging gives better results than thiessen, and the accuracy of both methods improves when external trends are incorporated. (c) 2006 Elsevier B.V. All rights reserved.
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页码:413 / 421
页数:9
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